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Databases · head to head

BigQuery vs Heap

BigQuery logo

BigQuery

Databases

Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.

From
Free
Rated
-
Heap logo

Heap

Technology

Product analytics for the modern product team

From
Free
Rated
-

The short version

  • Each has a real cost: BigQuery on-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.; Heap no built-in A/B testing or feature flags; requires integration with separate tools for experimentation
  • They diverge on capability: BigQuery covers Serverless compute, Heap covers Autocapture.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Heap actually diverge.

Attributes where BigQuery and Heap differ
AttributeBigQueryHeap
Pricing modelusage-basedUnknown
PlatformsWeb, Cloud APIWeb, iOS, Android
CategoryDatabasesTechnology
Founded20082013

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in BigQuery

  • Serverless compute
  • Separation of storage and compute
  • Two pricing models
  • Partitioning and clustering
  • Materialised views
  • BigQuery ML
  • Storage Write API
  • BI Engine

Only in Heap

  • Autocapture
  • Retroactive analytics
  • Session replay
  • Funnel analysis
  • User segmentation
  • Path analysis
  • Data science
  • Virtual events

What people use each for

The jobs each tool is most often brought in to do.

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Heap
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Heap
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Heap
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Heap

Heap

  • User behavior analysisnot BigQuery
  • Conversion optimizationnot BigQuery
  • Product adoptionnot BigQuery
  • Customer journey mappingnot BigQuery
  • A/B testing analysisnot BigQuery

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

BigQuery

  • On-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
  • There is no way to join tables that live in different regions, so a data estate split across regions for residency reasons has to be reconciled with copies and the storage and transfer that implies.
  • It is not built for point lookups; retrieving a single row has latency measured in hundreds of milliseconds or more, so BigQuery cannot serve an application's read path and always needs a second store in front of it.
  • Frequent small mutations run into DML concurrency limits and the cost of rewriting storage blocks, so a workload that updates individual rows continuously behaves badly compared with an append-only design.
  • The compute exists only inside Google Cloud, so while tables can be exported, the accumulated GoogleSQL, scheduled queries, authorised views, ML models and IAM structure do not move, and switching warehouses is a rewrite of the analytical layer.

Heap

  • No built-in A/B testing or feature flags; requires integration with separate tools for experimentation
  • Group analytics and advanced features require a sales conversation, not self-serve
  • Cloud-only deployment; no self-hosted option for data security or compliance requirements
  • Session replay lacks developer debugging tools compared to PostHog
  • Pricing for Growth and Pro plans requires direct sales contact; no transparency on how pricing scales

Pricing, plan by plan

BigQuery

Free
  • Free TierFree
    • 1TB queries/month
    • 10GB storage/month
    • Standard support
  • On-demand$6.25/TB
    • Pay per query
    • Pay per storage
    • All features

Heap

Free
  • FreeFree
    • Up to 10,000 monthly sessions
    • Basic charts
    • 6 months data history
  • Growth$undefined/custom
    • Custom session pricing
    • Sense AI assistant
    • 12 months data history
  • Pro$undefined/custom
    • Custom session pricing
    • Account analytics
    • Engagement matrix
  • Premier$undefined/custom
    • Custom session pricing
    • Data warehouse integration
    • Unlimited projects

Which should you pick?

Choose BigQuery if

  • You need serverless compute.
  • You want to start without paying.
  • You work on Web, Cloud API.
  • You also want separation of storage and compute.

Choose Heap if

  • You need autocapture.
  • You want to start without paying.
  • You work on Web, iOS, Android.
  • You also want retroactive analytics.

Questions people ask

Is BigQuery or Heap better?
Neither clearly leads. BigQuery starts at Free and Heap at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Heap?
BigQuery starts at Free and Heap at Free.
Does BigQuery or Heap run on more platforms?
BigQuery runs on Web, Cloud API. Heap runs on Web, iOS, Android.
Can I use BigQuery for free?
Both have a free tier, so you can try either at no cost before committing.
What is BigQuery best used for?
BigQuery is most often used for a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place, bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running, event and clickstream analytics ingested continuously through the storage write api and queried without a load window, analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portability. Of those, a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place and bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running are not what Heap is typically brought in for.
What can BigQuery do that Heap cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Heap covers Autocapture, Retroactive analytics, Session replay, Funnel analysis.

Answered from the vendors’ own pages

BigQuery: How is BigQuery actually billed?

Storage is billed separately from compute. Compute is either on-demand, priced by the bytes a query reads from the referenced columns, or capacity-based, where you reserve autoscaling slots. Most cost surprises come from on-demand queries that scan more than expected.

Heap: What does Heap's autocapture feature do?

Heap's autocapture is a single code snippet that automatically captures every click, swipe, tap, pageview, and form fill on your website and apps without requiring manual event setup. Once installed, Heap captures the entire digital experience of every user on every platform with no ongoing engineering maintenance needed.

Source
BigQuery: How do I control query cost?

Partition and cluster tables so queries prune data, select only the columns needed, use materialised views for repeated aggregations, and set maximum bytes billed on queries so a runaway scan fails instead of billing.

Heap: What are Heap's pricing plans and how much do they cost?

Heap offers a Free plan for up to 10,000 monthly sessions. Growth, Pro, and Premier plans use custom session-based pricing that requires contacting sales for a quote. Free includes basic charts and 6 months data history. Growth adds the Sense AI assistant. Pro adds account analytics. Premier adds data warehouse integration and dedicated customer success management.

Source
BigQuery: Can I use it without being on Google Cloud?

The service only runs on Google Cloud. BigQuery Omni can query data held in S3 or Azure storage, but the compute is still Google's and the account relationship is still with Google.

Heap: Does Heap include session replay and A/B testing?

Heap includes integrated session replay showing exactly what users did on your site. However, Heap does not include built-in A/B testing or feature flags. Teams requiring these capabilities must use separate tools or integrate with third-party platforms.

Source
BigQuery: Is it suitable for serving application queries?

No. Latency for single-row reads is far too high. BigQuery is an analytical warehouse and application read paths need a transactional database or a cache in front of it.

Heap: What integrations does Heap support?

Heap supports over 100 integrations connecting to business tools including marketing platforms, CRMs, and data warehouses. This allows insights to reach relevant teams and ensures data flows to other business systems automatically.

Source
BigQuery: When should I move from on-demand to capacity pricing?

When on-demand spend becomes both large and predictable, or when unpredictable spend is a bigger problem than query queueing. The switch trades a variable bill for a fixed one plus contention between workloads.

Heap: Does Heap offer self-hosting or is it cloud-only?

Heap is cloud-only and does not offer self-hosted options. Organizations requiring on-premises deployment should consider alternatives like PostHog which supports self-hosting alongside its cloud product.

Source
Heap: What is Sense and how does it help with analytics?

Sense Chat is Heap's AI assistant that enables users to access analytics without extensive technical knowledge. It allows teams to ask questions about user behavior and get answers directly without lengthy onboarding or technical expertise, making insights more accessible to non-technical stakeholders.

Source
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